This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time...This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.展开更多
基金supported by the National Natural Science Foundation of China(61072120)
文摘This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.
文摘针对多分量调频信号源混合相交时频分布盲分离,提出白化-均匀加权非正交联合对角化(Whitening-Uniformly Weighted Exhaustive Diagonalization using Gauss iteration,简称W-UWEDGE)方法估计混合矩阵。白化对相关信号去冗余处理,无需约束源信号概率密度形式,仅限制源之间整个时频面上无完全重合成分,非正交联合对角化则针对复数域。首先将非正交联合对角化可辨识性从时延平面推广至二次型时频平面,然后利用基于白化处理的梯度范数选择自项时频点(auto-time frequency point),进而利用均匀加权近似联合对角化方法估计混合矩阵,分析Amari error值随信噪比及时频矩阵个数的变化规律,与针对混合信号时间历程及时频分布的两类分离方法进行性能比较,显示出所提盲分离方法的优越性。最后应用于转子运行状态识别与齿轮复合故障源分离。仿真与实验数据分析表明所提出方法分离多分量调频相关源的有效性。
